Finding combinatorial drug repositioning therapy for Alzheimer's disease and related dementias

寻找治疗阿尔茨海默病和相关痴呆症的组合药物重新定位疗法

基本信息

批准号：
10377455
负责人：
Xiaoqian Jiang
金额：
$ 77.16万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10377455
关键词：
Adoption Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease related dementia Alzheimer&apos s disease therapy Big Data Biological Biological Markers Biology Cell model Cells Client Clinical Combination Drug Therapy Complex Computer Models Data Databases Dementia Development Disease Drug Combinations Drug Modelings Drug Targeting Drug resistance Electronic Health Record Equilibrium Fingerprint Genes Healthcare Informatics Infrastructure Investments Joints Knowledge Literature Methods Modeling Molecular Mus Neurodegenerative Disorders Ontology Outcome Pathway interactions Patients Pharmaceutical Preparations Pharmacogenomics Pharmacology Pharmacotherapy Phenotype Population Prevention Process Protective Agents Proteins Research Resource Sharing Risk Side Signal Pathway Source Speed Testing Toxic effect Validation big biomedical data combinatorial drug candidate drug discovery effective therapy efficacy validation experimental study gene interaction graph neural network in silico in vitro Model in vivo innovation knowledgebase multimodality novel novel drug combination novel therapeutics open source screening side effect simulation software development tool user-friendly

项目摘要

PROJECT SUMMARY AD/ADRD are highly complex diseases characterized by distinct molecular pathways and neuropathological phenotypes. Unfortunately, the treatment remains at best modestly effective and no new drugs have been approved since 2003. Combinatorial drug therapy for AD/ADRD treatment has not been intensively studied but it is highly promising. We hypothesize that finding repositioned drug combinations through innovative exploration of big data may uncover effective AD/ADRD treatments, with implicit advantages in overcoming drug resistance and targeting multiple biomarkers. We will combine big biomedical data from complementary sources, novel and advanced informatics models, clinical domain expertise, as well as biology knowledge and validation into a coherent framework to tackle AD/ADRD with potential combinatorial drug therapies. In an exponentially larger and more challenging space of combinatorial drug therapy, opportunities are also exponentially larger when compared with traditional single-drug models but many computational challenges need to be carefully handled. We will develop multiple computational models under two philosophical umbrellas, with focuses on quantifiable screening and biological understanding. Our findings will be validated with biological experiments from cell to mouse. If successful, we will significantly advance AD/ADRD research and benefit patients with safe and effective treatment.

项目概要 AD/ADRD 是高度复杂的疾病，其特征是不同的分子途径和神经病理学表型。不幸的是，这种治疗充其量只是有限的效果，而且还没有新药上市。自 2003 年起获得批准。针对 AD/ADRD 治疗的联合药物疗法尚未得到深入研究，但这是非常有前途的。我们假设通过创新找到重新定位的药物组合大数据的探索可能会发现有效的 AD/ADRD 治疗方法，并在克服 AD/ADRD 方面具有隐含的优势耐药性和针对多种生物标志物。我们将结合互补的生物医学大数据来源、新颖和先进的信息学模型、临床领域专业知识以及生物学知识和验证一个连贯的框架，以通过潜在的组合药物疗法来解决 AD/ADRD。在一个组合药物治疗的空间呈指数级增长且更具挑战性，机遇也随之而来与传统的单一药物模型相比呈指数级增长，但存在许多计算挑战需要小心处理。我们将在两种哲学下开发多种计算模型保护伞，重点是可量化的筛查和生物学理解。我们的发现将得到验证从细胞到小鼠的生物学实验。如果成功，我们将显着推进 AD/ADRD 研究使患者获得安全有效的治疗。